Identification of characteristic gene modules of osteosarcoma using bioinformatics analysis indicates the possible molecular pathogenesis

Li,Hongmin; He,Yangke; Hao,Peng; Liu,Pan

doi:10.3892/mmr.2017.6245

Identification of characteristic gene modules of osteosarcoma using bioinformatics analysis indicates the possible molecular pathogenesis

Authors:
- Hongmin Li
- Yangke He
- Peng Hao
- Pan Liu
View Affiliations

Affiliations: Cancer Center, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, Sichuan 610072, P.R. China, Department of Orthopedics, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, Sichuan 610072, P.R. China
Published online on: February 24, 2017 https://doi.org/10.3892/mmr.2017.6245
Pages: 2113-2119
Copyright: © Li et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

The aim of the present study was to investigate the possible pathogenesis of osteosarcoma using bioinformatics analysis to examine gene‑gene interactions. A total of three datasets associated with osteosarcoma were downloaded from the Gene Expression Omnibus. The differentially expressed genes (DEGs) were identified using the significance analysis of microarrays method, which then were subjected to the Human Protein Reference Database to identify the protein‑protein interaction (PPI) pairs and to construct a PPI network of the DEGs. Subsequent multilevel community analysis was applied to mine the modules in the network, followed by screening of the differential expression module using the GlobalAncova package. The genes in the differential expression modules were verified in the valid datasets. The verified genes underwent functional and pathway enrichment analysis. A total of 616 DEGs were selected to construct the PPI network, which included 5,808 osteosarcoma‑specific interaction pairs and 8,012 normal‑specific pairs. Tumor protein p53 (TP53), mitogen-activated protein kinase 1 (MAPK1) and estrogen receptor 1 (ESR1) were identified the most important osteosarcoma‑associated genes, with the highest levels of topological properties. Neurogenic locus notch homolog protein 3 (NOTCH3) and caspase 1 (CASP1) were identified as the osteosarcoma‑specific interaction pairs. Among all 23 mined modules, three were identified as differential expression modules, which were verified in the other two datasets. The genes in these modules were predominantly enriched in the FGFR, MAPK and Notch signaling pathways. Therefore, TP53, MAPK1, ESR1, NOTCH3 and CASP1 may be important in the development of osteosarcoma, and provides valuable clues to investigate the pathogenesis of osteosarcoma using the three differential expression modules.

View Figures

View References

1	Mirabello LJ, Yeager M, Mai PL, Gastier-Foster JM, Gorlick R, Khanna C, Patiño-Garcia A, Sierrasesúmaga L, Lecanda F, Andrulis IL, et al: Abstract 5574: High prevalence of germline TP53 mutations in young osteosarcoma cases. Cancer Res. 75 Suppl:S55742015. View Article : Google Scholar
2	Mirabello L, Troisi RJ and Savage SA: Osteosarcoma incidence and survival rates from 1973 to 2004: Data from the surveillance, epidemiology, and end results program. Cancer. 115:1531–1543. 2009. View Article : Google Scholar : PubMed/NCBI
3	Lussier DM, Johnson JL, Hingorani P and Blattman JN: Combination immunotherapy with α-CTLA-4 and α-PD-L1 antibody blockade prevents immune escape and leads to complete control of metastatic osteosarcoma. J Immunother Cancer. 3:212015. View Article : Google Scholar : PubMed/NCBI
4	Carrle D and Bielack S: Osteosarcoma lung metastases detection and principles of multimodal therapyPediatric and Adolescent Osteosarcoma. Springer; New York, NY: pp. 165–184. 2010
5	Betts JA, French JD, Brown MA and Edwards SL: Long-range transcriptional regulation of breast cancer genes. Genes Chromosomes Cancer. 52:113–125. 2013. View Article : Google Scholar : PubMed/NCBI
6	Hall MA, Verma SS, Wallace J, Lucas A, Berg RL, Connolly J, Crawford DC, Crosslin DR, De Andrade M, Doheny KF, et al: Biology-driven gene-gene interaction analysis of age-related cataract in the eMERGE network. Genet Epidemiol. 39:376–384. 2015. View Article : Google Scholar : PubMed/NCBI
7	Kallberg H, Padyukov L, Plenge RM, Ronnelid J, Gregersen PK, van der Helm-van Mil AH, Toes RE, Huizinga TW, Klareskog L and Alfredsson L: Epidemiological Investigation of Rheumatoid Arthritis study group: Gene-gene and gene-environment interactions involving HLA-DRB1, PTPN22 and smoking in two subsets of rheumatoid arthritis. Am J Hum Genet. 80:867–875. 2007. View Article : Google Scholar : PubMed/NCBI
8	Seddighzadeh M, Korotkova M, Källberg H, Ding B, Daha N, Kurreeman FA, Toes RE, Huizinga TW, Catrina AI, Alfredsson L, et al: Evidence for interaction between 5-hydroxytryptamine (serotonin) receptor 2A and MHC type II molecules in the development of rheumatoid arthritis. Eur J Hum Genet. 18:821–826. 2010. View Article : Google Scholar : PubMed/NCBI
9	Park J, Kim I, Jung KJ, Kim S, Jee SH and Yoon SK: Gene-gene interaction analysis identifies a new genetic risk factor for colorectal cancer. J Biomed Sci. 22:732015. View Article : Google Scholar : PubMed/NCBI
10	Shen Y, Xun G, Guo H, He Y, Ou J, Dong H, Xia K and Zhao J: Association and gene-gene interactions study of reelin signaling pathway related genes with autism in the Han Chinese population. Autism Res. 9:436–442. 2016. View Article : Google Scholar : PubMed/NCBI
11	Wang YR, Jiang K, Feldman LJ, Bickel PJ and Huang H: Inferring gene-gene interactions and functional modules using sparse canonical correlation analysis. Ann Appl Stat. 9:300–323. 2015. View Article : Google Scholar
12	Kresse SH, Rydbeck H, Skårn M, Namløs HM, Barragan-Polania AH, Cleton-Jansen AM, Serra M, Liestøl K, Hogendoorn PC, Hovig E, et al: Integrative analysis reveals relationships of genetic and epigenetic alterations in osteosarcoma. PLoS One. 7:e482622012. View Article : Google Scholar : PubMed/NCBI
13	Endo-Munoz L, Cumming A, Sommerville S, Dickinson I and Saunders NA: Osteosarcoma is characterised by reduced expression of markers of osteoclastogenesis and antigen presentation compared with normal bone. Br J Cancer. 103:73–81. 2010. View Article : Google Scholar : PubMed/NCBI
14	Paoloni M, Davis S, Lana S, Withrow S, Sangiorgi L, Picci P, Hewitt S, Triche T, Meltzer P and Khanna C: Canine tumor cross-species genomics uncovers targets linked to osteosarcoma progression. BMC Genomics. 10:6252009. View Article : Google Scholar : PubMed/NCBI
15	Grace C and Nacheva EP: Significance analysis of microarrays (SAM) offers clues to differences between the genomes of adult Philadelphia positive ALL and the lymphoid blast transformation of CML. Cancer Inform. 11:173–183. 2012. View Article : Google Scholar : PubMed/NCBI
16	McClure R, Balasubramanian D, Sun Y, Bobrovskyy M, Sumby P, Genco CA, Vanderpool CK and Tjaden B: Computational analysis of bacterial RNA-Seq data. Nucleic Acids Res. 41:e1402013. View Article : Google Scholar : PubMed/NCBI
17	McDermott JE, Diamond DL, Corley C, Rasmussen AL, Katze MG and Waters KM: Topological analysis of protein co-abundance networks identifies novel host targets important for HCV infection and pathogenesis. BMC Syst Biol. 6:282012. View Article : Google Scholar : PubMed/NCBI
18	Subelj L and Bajec M: Unfolding communities in large complex networks: Combining defensive and offensive label propagation for core extraction. Phys Rev E Stat Nonlin Soft Matter Phys. 83:0361032011. View Article : Google Scholar : PubMed/NCBI
19	Liu W, Yue F, Zheng M, Merlot A, Bae DH, Huang M, Lane D, Jansson P, Lui GY, Richardson V, et al: The proto-oncogene c-Src and its downstream signaling pathways are inhibited by the metastasis suppressor, NDRG1. Oncotarget. 6:8851–8874. 2015. View Article : Google Scholar : PubMed/NCBI
20	Bartolomé RA, García-Palmero I, Torres S, López-Lucendo M, Balyasnikova IV and Casal JI: IL-13 receptor α2 signaling requires a scaffold protein, FAM120A, to activate the FAK and PI3K pathways in colon cancer metastasis. Cancer Res. 75:2434–2444. 2015. View Article : Google Scholar : PubMed/NCBI
21	Correction: Associations and Interactions between Ets-1 and Ets-2 and Coregulatory proteins, SRC-1, AIB1, and NCoR in breast cancer. Clin Cancer Res. 21:2190–2191. 2015. View Article : Google Scholar : PubMed/NCBI
22	Guarino M: Src signaling in cancer invasion. J Cell Physiol. 223:14–26. 2010.PubMed/NCBI
23	Hu C, Deng Z, Zhang Y, Yan L, Cai L, Lei J and Xie Y: The prognostic significance of Src and p-Src expression in patients with osteosarcoma. Med Sci Monit. 21:638–645. 2015. View Article : Google Scholar : PubMed/NCBI
24	Sonaglio V, De Carvalho AC, Toledo SR, Salinas-Souza C, Carvalho AL, Petrilli AS, de Camargo B and Vettore AL: Aberrant DNA methylation of ESR1 and p14ARF genes could be useful as prognostic indicators in osteosarcoma. Onco Targets Ther. 6:713–723. 2013.PubMed/NCBI
25	McIntyre JF, Smith-Sorensen B, Friend SH, Kassell J, Borresen AL, Yan YX, Russo C, Sato J, Barbier N, Miser J, et al: Germline mutations of the p53 tumor suppressor gene in children with osteosarcoma. J Clin Oncol. 12:925–930. 1994. View Article : Google Scholar : PubMed/NCBI
26	Song R, Tian K, Wang W and Wang L: P53 suppresses cell proliferation, metastasis, and angiogenesis of osteosarcoma through inhibition of the PI3K/AKT/mTOR pathway. Int J Surg. 20:80–87. 2015. View Article : Google Scholar : PubMed/NCBI
27	Stossi F, Barnett DH, Frasor J, Komm B, Lyttle CR and Katzenellenbogen BS: Transcriptional profiling of estrogen-regulated gene expression via estrogen receptor (ER) alpha or ERbeta in human osteosarcoma cells: Distinct and common target genes for these receptors. Endocrinology. 145:3473–3486. 2004. View Article : Google Scholar : PubMed/NCBI
28	Yi F, Amarasinghe B and Dang TP: Manic fringe inhibits tumor growth by suppressing Notch3 degradation in lung cancer. Am J Cancer Res. 3:490–499. 2013.PubMed/NCBI
29	Li R, Zhang W, Cui J, Shui W, Yin L, Wang Y, Zhang H, Wang N, Wu N, Nan G, et al: Targeting BMP9-promoted human osteosarcoma growth by inactivation of notch signaling. Curr Cancer Drug Targets. 14:274–285. 2014. View Article : Google Scholar : PubMed/NCBI
30	Guo Y and Kyprianou N: Restoration of transforming growth factor beta signaling pathway in human prostate cancer cells suppresses tumorigenicity via induction of caspase-1-mediated apoptosis. Cancer Res. 59:1366–1371. 1999.PubMed/NCBI
31	Whyte M: ICE/CED-3 proteasesin apoptosis. Trends Cell Biol. 6:245–248. 1996. View Article : Google Scholar : PubMed/NCBI